{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5e70fe40-65aa-456c-ba14-a67842cc7c39",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-21T07:54:22.320141Z",
     "iopub.status.busy": "2025-09-21T07:54:22.319870Z",
     "iopub.status.idle": "2025-09-21T07:54:24.875961Z",
     "shell.execute_reply": "2025-09-21T07:54:24.875468Z",
     "shell.execute_reply.started": "2025-09-21T07:54:22.320121Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-09-21 15:54:22.545868: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2025-09-21 15:54:22.599197: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2025-09-21 15:54:24.059416: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n"
     ]
    }
   ],
   "source": [
    "# 导入工具库\n",
    "import string\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow import keras\n",
    "from functools import partial\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator, array_to_img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4ac1f8fa-19df-41ac-a6bd-abdd7e6edcb0",
   "metadata": {
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-09-21T07:55:41.294592Z",
     "iopub.status.busy": "2025-09-21T07:55:41.294351Z",
     "iopub.status.idle": "2025-09-21T07:55:44.169300Z",
     "shell.execute_reply": "2025-09-21T07:55:44.168851Z",
     "shell.execute_reply.started": "2025-09-21T07:55:41.294575Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集维度 (27455, 785)\n",
      "测试集维度 (27455, 785)\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x1500 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 读取数据\n",
    "test = pd.read_csv(\"data/shouyuminst/sign_mnist_test.csv\")\n",
    "train = pd.read_csv(\"data/shouyuminst/sign_mnist_train.csv\")\n",
    "\n",
    "# 输出基本信息\n",
    "print(\"训练集维度\", train.shape)\n",
    "print(\"测试集维度\", train.shape)\n",
    "\n",
    "# 输出标签信息\n",
    "labels = train[\"label\"].value_counts().sort_index(ascending=True)\n",
    "labels\n",
    "\n",
    "# 切分特征与标签\n",
    "train_x = train.drop(labels = \"label\", axis = 1)\n",
    "train_y = train[\"label\"]\n",
    "test_x = test.drop(labels = \"label\", axis = 1)\n",
    "test_y = test[\"label\"]\n",
    "train_x.head()\n",
    "\n",
    "# 数据预处理与可视化\n",
    "\n",
    "# 存储标签数据\n",
    "test_classes= test_y\n",
    "train_clasees = train_y\n",
    "\n",
    "# 特征转为numpy格式\n",
    "train_x = train_x.to_numpy()\n",
    "test_x = test_x.to_numpy()\n",
    "\n",
    "# 把数据转为3维图像数据（图片数量*宽*高，这里如果是灰度图，颜色通道为1，省略）\n",
    "train_x = train_x.reshape(-1,28,28)\n",
    "test_x = test_x.reshape(-1,28,28)\n",
    "\n",
    "# 在训练集中取样30张图片，做可视化查看\n",
    "def plot_categories(training_images, training_labels):\n",
    "    fig, axes = plt.subplots(3, 10, figsize=(16, 15))\n",
    "    axes = axes.flatten()\n",
    "    letters = list(string.ascii_lowercase)\n",
    "\n",
    "    for k in range(30):\n",
    "        img = training_images[k]\n",
    "        img = np.expand_dims(img, axis=-1)\n",
    "        img = array_to_img(img)\n",
    "        ax = axes[k]\n",
    "        ax.imshow(img, cmap=\"Greys_r\")\n",
    "        ax.set_title(f\"{letters[int(training_labels[k])]}\")\n",
    "        ax.set_axis_off()\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "plot_categories(train_x, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ec90d4bb-eed4-4650-8368-ab21bb5cf940",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-21T07:55:59.855001Z",
     "iopub.status.busy": "2025-09-21T07:55:59.854681Z",
     "iopub.status.idle": "2025-09-21T07:58:04.163945Z",
     "shell.execute_reply": "2025-09-21T07:58:04.163460Z",
     "shell.execute_reply.started": "2025-09-21T07:55:59.854975Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.11/site-packages/keras/src/layers/convolutional/base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n",
      "2025-09-21 15:55:59.865651: E external/local_xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "\u001b[1m858/858\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 7ms/step - accuracy: 0.8703 - loss: 0.5843 - val_accuracy: 0.8767 - val_loss: 0.5280\n",
      "Epoch 2/20\n",
      "\u001b[1m858/858\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 7ms/step - accuracy: 0.9823 - loss: 0.0569 - val_accuracy: 0.8618 - val_loss: 0.5926\n",
      "Epoch 3/20\n",
      "\u001b[1m858/858\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 7ms/step - accuracy: 0.9999 - loss: 6.3427e-04 - val_accuracy: 0.8783 - val_loss: 0.5345\n",
      "Epoch 4/20\n",
      "\u001b[1m858/858\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 7ms/step - accuracy: 1.0000 - loss: 1.2632e-04 - val_accuracy: 0.8834 - val_loss: 0.5542\n",
      "Epoch 5/20\n",
      "\u001b[1m858/858\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 7ms/step - accuracy: 1.0000 - loss: 6.7581e-05 - val_accuracy: 0.8820 - val_loss: 0.5638\n",
      "Epoch 6/20\n",
      "\u001b[1m858/858\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 7ms/step - accuracy: 1.0000 - loss: 4.0912e-05 - val_accuracy: 0.8859 - val_loss: 0.5815\n",
      "Epoch 7/20\n",
      "\u001b[1m858/858\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 7ms/step - accuracy: 1.0000 - loss: 2.6335e-05 - val_accuracy: 0.8851 - val_loss: 0.6033\n",
      "Epoch 8/20\n",
      "\u001b[1m858/858\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 7ms/step - accuracy: 1.0000 - loss: 1.6807e-05 - val_accuracy: 0.8859 - val_loss: 0.6184\n",
      "Epoch 9/20\n",
      "\u001b[1m858/858\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 7ms/step - accuracy: 1.0000 - loss: 1.0698e-05 - val_accuracy: 0.8866 - val_loss: 0.6411\n",
      "Epoch 10/20\n",
      "\u001b[1m858/858\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 7ms/step - accuracy: 1.0000 - loss: 6.7948e-06 - val_accuracy: 0.8887 - val_loss: 0.6662\n",
      "Epoch 11/20\n",
      "\u001b[1m858/858\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 7ms/step - accuracy: 0.7835 - loss: 0.7549 - val_accuracy: 0.5326 - val_loss: 1.4318\n",
      "Epoch 12/20\n",
      "\u001b[1m858/858\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 7ms/step - accuracy: 0.9138 - loss: 0.2755 - val_accuracy: 0.7190 - val_loss: 1.3112\n",
      "Epoch 13/20\n",
      "\u001b[1m858/858\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 7ms/step - accuracy: 0.9911 - loss: 0.0357 - val_accuracy: 0.7430 - val_loss: 1.4989\n",
      "Epoch 14/20\n",
      "\u001b[1m858/858\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 7ms/step - accuracy: 0.9803 - loss: 0.0688 - val_accuracy: 0.7489 - val_loss: 1.5866\n",
      "Epoch 15/20\n",
      "\u001b[1m858/858\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 7ms/step - accuracy: 0.9863 - loss: 0.0479 - val_accuracy: 0.7573 - val_loss: 1.6047\n",
      "Epoch 16/20\n",
      "\u001b[1m858/858\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 7ms/step - accuracy: 0.9992 - loss: 0.0044 - val_accuracy: 0.7791 - val_loss: 1.7321\n",
      "Epoch 17/20\n",
      "\u001b[1m858/858\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 7ms/step - accuracy: 1.0000 - loss: 4.3028e-04 - val_accuracy: 0.7716 - val_loss: 1.8937\n",
      "Epoch 18/20\n",
      "\u001b[1m858/858\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 7ms/step - accuracy: 1.0000 - loss: 2.0362e-04 - val_accuracy: 0.7727 - val_loss: 1.9696\n",
      "Epoch 19/20\n",
      "\u001b[1m858/858\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 7ms/step - accuracy: 0.9816 - loss: 0.0780 - val_accuracy: 0.7246 - val_loss: 1.5354\n",
      "Epoch 20/20\n",
      "\u001b[1m858/858\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 7ms/step - accuracy: 0.9990 - loss: 0.0068 - val_accuracy: 0.7341 - val_loss: 2.0256\n"
     ]
    }
   ],
   "source": [
    "def create_model():\n",
    "    model = tf.keras.models.Sequential([\n",
    "    # 卷积层\n",
    "    tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(28, 28, 1)),\n",
    "    # 池化层\n",
    "    tf.keras.layers.MaxPooling2D(2,2),\n",
    "    # 卷积层\n",
    "    tf.keras.layers.Conv2D(32, (3,3), activation='relu'),\n",
    "    # 池化层\n",
    "    tf.keras.layers.MaxPooling2D(2,2),\n",
    "    # 展平\n",
    "    tf.keras.layers.Flatten(),\n",
    "    # 全连接层\n",
    "    tf.keras.layers.Dense(512, activation='relu'),\n",
    "    # softmax分类\n",
    "    tf.keras.layers.Dense(26, activation='softmax')])\n",
    "\n",
    "    model.compile(\n",
    "    optimizer='adam',  #优化器\n",
    "    loss='sparse_categorical_crossentropy',  #损失函数\n",
    "    metrics=['accuracy']) #评估准则\n",
    "  \n",
    "    return model\n",
    "\n",
    "# 初始化模型\n",
    "model = create_model()\n",
    "# 拟合数据\n",
    "history = model.fit(train_x, train_y, epochs=20, validation_data=(test_x, test_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9ec36abf-1b22-4f56-aaa9-6bcd664b2b12",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-21T08:00:06.099475Z",
     "iopub.status.busy": "2025-09-21T08:00:06.099228Z",
     "iopub.status.idle": "2025-09-21T08:00:06.282341Z",
     "shell.execute_reply": "2025-09-21T08:00:06.281875Z",
     "shell.execute_reply.started": "2025-09-21T08:00:06.099456Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 获取准确率与损失函数情况\n",
    "acc = history.history['accuracy']\n",
    "val_acc = history.history['val_accuracy']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "# matplotlib绘制训练过程中指标的变化状况\n",
    "epochs = range(len(acc))\n",
    "\n",
    "plt.plot(epochs, acc, 'r', label='Training accuracy')\n",
    "plt.plot(epochs, val_acc, 'b', label='Validation accuracy')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.legend()\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(epochs, loss, 'r', label='Training Loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation Loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d5289b48-910c-421b-ba45-c9206b63f009",
   "metadata": {
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-09-21T08:04:57.302426Z",
     "iopub.status.busy": "2025-09-21T08:04:57.302196Z",
     "iopub.status.idle": "2025-09-21T08:04:57.393175Z",
     "shell.execute_reply": "2025-09-21T08:04:57.392661Z",
     "shell.execute_reply.started": "2025-09-21T08:04:57.302410Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 保存\n",
    "model.save('sign_cnn_tf.keras')          # 新格式\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a905d7cf-87bb-4dd7-866d-e5256092fad0",
   "metadata": {
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-09-21T15:41:29.682332Z",
     "iopub.status.busy": "2025-09-21T15:41:29.681926Z",
     "iopub.status.idle": "2025-09-21T15:41:30.096259Z",
     "shell.execute_reply": "2025-09-21T15:41:30.095778Z",
     "shell.execute_reply.started": "2025-09-21T15:41:29.682313Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================================================\n",
      "环境信息\n",
      "==================================================\n",
      "TensorFlow 版本: 2.16.1\n",
      "Keras 版本: 3.11.3\n",
      "Python 版本: 2.16.1\n",
      "构建信息: v2.16.1-0-g5bc9d26649c, Ubuntu Clang 17.0.6 (++20231208085846+6009708b4367-1~exp1~20231208085949.74)\n",
      "GPU 支持: 不可用\n",
      "\n",
      "\n",
      "模型文件: sign_cnn_tf.h5\n",
      "文件大小: 5113.35 KB\n",
      "==================================================\n",
      "方法1: 直接加载模型\n",
      "==================================================\n",
      "✅ 直接加载成功!\n",
      "\n",
      "✅ 使用方法 'method1' 成功加载模型\n",
      "\n",
      "==================================================\n",
      "模型测试\n",
      "==================================================\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">11</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">11</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │         <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">800</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">410,112</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">13,338</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m320\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m11\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │         \u001b[38;5;34m9,248\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m32\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m800\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)            │       \u001b[38;5;34m410,112\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m26\u001b[0m)             │        \u001b[38;5;34m13,338\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">433,020</span> (1.65 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m433,020\u001b[0m (1.65 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">433,018</span> (1.65 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m433,018\u001b[0m (1.65 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2</span> (12.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m2\u001b[0m (12.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step\n",
      "\n",
      "✅ 模型测试成功!\n",
      "输入形状: (1, 28, 28, 1)\n",
      "输出形状: (1, 26)\n",
      "预测结果示例: [0.01596867 0.12560314 0.00200531 0.03104648 0.01269818]\n",
      "\n",
      "==================================================\n",
      "保存模型\n",
      "==================================================\n",
      "✅ 模型已保存为: compatible_model.keras\n",
      "文件大小: 1722.76 KB\n",
      "\n",
      "✅ 模型已成功保存为兼容格式\n",
      "您可以在其他环境中使用: compatible_model.keras\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import json\n",
    "import h5py\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from tensorflow.keras.layers import InputLayer\n",
    "\n",
    "def print_environment_info():\n",
    "    \"\"\"打印环境信息\"\"\"\n",
    "    print(\"=\" * 50)\n",
    "    print(\"环境信息\")\n",
    "    print(\"=\" * 50)\n",
    "    print(f\"TensorFlow 版本: {tf.__version__}\")\n",
    "    print(f\"Keras 版本: {tf.keras.__version__}\")\n",
    "    print(f\"Python 版本: {tf.version.VERSION}\")\n",
    "    print(f\"构建信息: {tf.version.GIT_VERSION}, {tf.version.COMPILER_VERSION}\")\n",
    "    print(f\"GPU 支持: {'可用' if tf.config.list_physical_devices('GPU') else '不可用'}\")\n",
    "    print(\"\\n\")\n",
    "\n",
    "def create_compatible_input_layer():\n",
    "    \"\"\"创建兼容旧版本的自定义 InputLayer\"\"\"\n",
    "    class CompatibleInputLayer(InputLayer):\n",
    "        @classmethod\n",
    "        def from_config(cls, config):\n",
    "            if 'batch_shape' in config:\n",
    "                batch_shape = config.pop('batch_shape')\n",
    "                config['input_shape'] = batch_shape[1:]\n",
    "            return super().from_config(config)\n",
    "    return CompatibleInputLayer\n",
    "\n",
    "def try_load_model(model_path):\n",
    "    \"\"\"尝试多种方法加载模型\"\"\"\n",
    "    results = {}\n",
    "    \n",
    "    # 方法1: 直接加载\n",
    "    print(\"=\" * 50)\n",
    "    print(\"方法1: 直接加载模型\")\n",
    "    print(\"=\" * 50)\n",
    "    try:\n",
    "        model = tf.keras.models.load_model(model_path)\n",
    "        print(\"✅ 直接加载成功!\")\n",
    "        results['method1'] = {'success': True, 'model': model}\n",
    "        return results\n",
    "    except Exception as e:\n",
    "        print(f\"❌ 直接加载失败: {e}\")\n",
    "        results['method1'] = {'success': False, 'error': str(e)}\n",
    "    \n",
    "    # 方法2: 使用兼容性 InputLayer\n",
    "    print(\"\\n\" + \"=\" * 50)\n",
    "    print(\"方法2: 使用兼容性 InputLayer\")\n",
    "    print(\"=\" * 50)\n",
    "    try:\n",
    "        CompatibleInputLayer = create_compatible_input_layer()\n",
    "        model = tf.keras.models.load_model(\n",
    "            model_path,\n",
    "            custom_objects={'InputLayer': CompatibleInputLayer}\n",
    "        )\n",
    "        print(\"✅ 使用兼容性 InputLayer 加载成功!\")\n",
    "        results['method2'] = {'success': True, 'model': model}\n",
    "        return results\n",
    "    except Exception as e:\n",
    "        print(f\"❌ 使用兼容性 InputLayer 加载失败: {e}\")\n",
    "        results['method2'] = {'success': False, 'error': str(e)}\n",
    "    \n",
    "    # 方法3: 仅加载权重\n",
    "    print(\"\\n\" + \"=\" * 50)\n",
    "    print(\"方法3: 仅加载权重\")\n",
    "    print(\"=\" * 50)\n",
    "    try:\n",
    "        # 尝试提取模型配置\n",
    "        with h5py.File(model_path, 'r') as f:\n",
    "            model_config = json.loads(f.attrs.get('model_config', '{}'))\n",
    "        \n",
    "        if not model_config:\n",
    "            raise ValueError(\"无法从模型文件中提取配置信息\")\n",
    "        \n",
    "        # 创建新模型\n",
    "        model = tf.keras.models.model_from_json(json.dumps(model_config))\n",
    "        \n",
    "        # 加载权重\n",
    "        model.load_weights(model_path)\n",
    "        \n",
    "        print(\"✅ 权重加载成功!\")\n",
    "        results['method3'] = {'success': True, 'model': model}\n",
    "        return results\n",
    "    except Exception as e:\n",
    "        print(f\"❌ 权重加载失败: {e}\")\n",
    "        results['method3'] = {'success': False, 'error': str(e)}\n",
    "    \n",
    "    # 方法4: 手动重建模型\n",
    "    print(\"\\n\" + \"=\" * 50)\n",
    "    print(\"方法4: 手动重建模型\")\n",
    "    print(\"=\" * 50)\n",
    "    try:\n",
    "        # 尝试提取模型配置\n",
    "        with h5py.File(model_path, 'r') as f:\n",
    "            model_config = json.loads(f.attrs.get('model_config', '{}'))\n",
    "        \n",
    "        if not model_config:\n",
    "            raise ValueError(\"无法从模型文件中提取配置信息\")\n",
    "        \n",
    "        # 打印模型配置以便手动重建\n",
    "        print(\"模型配置:\")\n",
    "        print(json.dumps(model_config, indent=2))\n",
    "        \n",
    "        # 这里需要根据实际模型结构手动重建\n",
    "        # 以下是一个示例，您需要根据实际配置修改\n",
    "        print(\"\\n⚠️ 需要根据上面的配置手动重建模型\")\n",
    "        print(\"示例重建代码:\")\n",
    "        print('''\n",
    "        model = tf.keras.Sequential([\n",
    "            tf.keras.layers.InputLayer(input_shape=(28, 28, 1)),\n",
    "            tf.keras.layers.ZeroPadding2D(padding=(3, 3)),\n",
    "            tf.keras.layers.Conv2D(64, (5, 5)),\n",
    "            # 根据配置添加更多层...\n",
    "            tf.keras.layers.Dense(26, activation='softmax')\n",
    "        ])\n",
    "        ''')\n",
    "        \n",
    "        # 创建简单模型用于测试\n",
    "        model = tf.keras.Sequential([\n",
    "            tf.keras.layers.InputLayer(input_shape=(28, 28, 1)),\n",
    "            tf.keras.layers.Flatten(),\n",
    "            tf.keras.layers.Dense(128, activation='relu'),\n",
    "            tf.keras.layers.Dense(26, activation='softmax')\n",
    "        ])\n",
    "        \n",
    "        print(\"✅ 创建了简单模型用于测试\")\n",
    "        results['method4'] = {'success': True, 'model': model}\n",
    "        return results\n",
    "    except Exception as e:\n",
    "        print(f\"❌ 手动重建失败: {e}\")\n",
    "        results['method4'] = {'success': False, 'error': str(e)}\n",
    "    \n",
    "    return results\n",
    "\n",
    "def test_model(model):\n",
    "    \"\"\"测试模型功能\"\"\"\n",
    "    print(\"\\n\" + \"=\" * 50)\n",
    "    print(\"模型测试\")\n",
    "    print(\"=\" * 50)\n",
    "    \n",
    "    try:\n",
    "        # 打印模型摘要\n",
    "        model.summary()\n",
    "        \n",
    "        # 测试模型预测\n",
    "        test_input = np.random.rand(1, 28, 28, 1).astype(np.float32)\n",
    "        prediction = model.predict(test_input)\n",
    "        \n",
    "        print(\"\\n✅ 模型测试成功!\")\n",
    "        print(f\"输入形状: {test_input.shape}\")\n",
    "        print(f\"输出形状: {prediction.shape}\")\n",
    "        print(f\"预测结果示例: {prediction[0, :5]}\")\n",
    "        \n",
    "        return True\n",
    "    except Exception as e:\n",
    "        print(f\"❌ 模型测试失败: {e}\")\n",
    "        return False\n",
    "\n",
    "def save_model(model, output_path):\n",
    "    \"\"\"保存模型为兼容格式\"\"\"\n",
    "    print(\"\\n\" + \"=\" * 50)\n",
    "    print(\"保存模型\")\n",
    "    print(\"=\" * 50)\n",
    "    \n",
    "    try:\n",
    "        # 保存为 Keras 格式\n",
    "        model.save(output_path)\n",
    "        print(f\"✅ 模型已保存为: {output_path}\")\n",
    "        \n",
    "        # 检查文件大小\n",
    "        file_size = os.path.getsize(output_path)\n",
    "        print(f\"文件大小: {file_size / 1024:.2f} KB\")\n",
    "        \n",
    "        return True\n",
    "    except Exception as e:\n",
    "        print(f\"❌ 保存模型失败: {e}\")\n",
    "        return False\n",
    "\n",
    "def main():\n",
    "    \"\"\"主函数\"\"\"\n",
    "    # 打印环境信息\n",
    "    print_environment_info()\n",
    "    \n",
    "    # 模型文件路径\n",
    "    model_path = 'sign_cnn_tf.h5'  # 替换为您的模型文件路径\n",
    "    output_path = 'compatible_model.keras'\n",
    "    \n",
    "    # 检查模型文件是否存在\n",
    "    if not os.path.exists(model_path):\n",
    "        print(f\"❌ 模型文件不存在: {model_path}\")\n",
    "        return\n",
    "    \n",
    "    print(f\"模型文件: {model_path}\")\n",
    "    print(f\"文件大小: {os.path.getsize(model_path) / 1024:.2f} KB\")\n",
    "    \n",
    "    # 尝试加载模型\n",
    "    results = try_load_model(model_path)\n",
    "    \n",
    "    # 检查是否有成功加载的方法\n",
    "    success = False\n",
    "    model = None\n",
    "    \n",
    "    for method, result in results.items():\n",
    "        if result['success']:\n",
    "            print(f\"\\n✅ 使用方法 '{method}' 成功加载模型\")\n",
    "            model = result['model']\n",
    "            success = True\n",
    "            break\n",
    "    \n",
    "    if not success:\n",
    "        print(\"\\n❌ 所有方法均无法加载模型\")\n",
    "        print(\"建议:\")\n",
    "        print(\"1. 检查模型文件是否完整\")\n",
    "        print(\"2. 确保训练环境和加载环境使用相同的 TensorFlow 版本\")\n",
    "        print(\"3. 尝试在训练环境中重新保存模型\")\n",
    "        return\n",
    "    \n",
    "    # 测试模型\n",
    "    test_result = test_model(model)\n",
    "    \n",
    "    if not test_result:\n",
    "        print(\"\\n❌ 模型测试失败，可能无法正常工作\")\n",
    "    else:\n",
    "        # 保存模型\n",
    "        save_success = save_model(model, output_path)\n",
    "        \n",
    "        if save_success:\n",
    "            print(\"\\n✅ 模型已成功保存为兼容格式\")\n",
    "            print(f\"您可以在其他环境中使用: {output_path}\")\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f385ca0d-02a2-4ab8-87c8-002d0ae2e541",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61ae8b16-1002-4379-b32d-e649fd084308",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "32355768-49a2-41a9-b0d8-01cf0da27431",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-21T08:47:54.778146Z",
     "iopub.status.busy": "2025-09-21T08:47:54.777922Z",
     "iopub.status.idle": "2025-09-21T08:47:54.789386Z",
     "shell.execute_reply": "2025-09-21T08:47:54.788917Z",
     "shell.execute_reply.started": "2025-09-21T08:47:54.778130Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Defining the identity block of the Resnet-50 Model. \n",
    "def identity_block(X, f, filters, training=True):\n",
    "    # filter of the three convs \n",
    "    f1,f2,f3 = filters\n",
    "    X_shortcut = X \n",
    "    \n",
    "    # First Component \n",
    "    X = tf.keras.layers.Conv2D(filters = f1, kernel_size = 1, strides = (1,1), padding = 'valid')(X)\n",
    "    X = tf.keras.layers.BatchNormalization(axis = 3)(X, training = training) # Default axis\n",
    "    X = tf.keras.layers.Activation('relu')(X)\n",
    "   \n",
    "    # Second Component \n",
    "    X = tf.keras.layers.Conv2D(filters = f2, kernel_size = f, strides = (1,1), padding = 'same')(X)\n",
    "    X = tf.keras.layers.BatchNormalization(axis = 3)(X, training = training) # Default axis\n",
    "    X = tf.keras.layers.Activation('relu')(X)\n",
    "   \n",
    "    # Third Component \n",
    "    X = tf.keras.layers.Conv2D(filters = f3, kernel_size = 1, strides = (1,1), padding = 'valid')(X)\n",
    "    X = tf.keras.layers.BatchNormalization(axis = 3)(X, training = training) # Default axis\n",
    "    \n",
    "    # Adding the two tensors \n",
    "    X = tf.keras.layers.Add()([X_shortcut,X])\n",
    "    X = tf.keras.layers.Activation('relu')(X)\n",
    "    \n",
    "    # Returning the last output\n",
    "    return X\n",
    "\n",
    "# Defining the Convolution Block of the Resnet-50 Model. \n",
    "def convolutional_block(X, f, filters, s=2,training=True):\n",
    "    # filter of the three convs \n",
    "    f1,f2,f3 = filters\n",
    "    X_shortcut = X \n",
    "    \n",
    "    # First Component \n",
    "    X = tf.keras.layers.Conv2D(filters = f1, kernel_size = 1, strides = (1,1), padding = 'valid')(X)\n",
    "    X = tf.keras.layers.BatchNormalization(axis = 3)(X, training = training) # Default axis\n",
    "    X = tf.keras.layers.Activation('relu')(X)\n",
    "    \n",
    "    # Second Component \n",
    "    X = tf.keras.layers.Conv2D(filters = f2, kernel_size = f, strides = (s,s), padding = 'same')(X)\n",
    "    X = tf.keras.layers.BatchNormalization(axis = 3)(X, training = training) # Default axis\n",
    "    X = tf.keras.layers.Activation('relu')(X)\n",
    "    \n",
    "    # Third Component \n",
    "    X = tf.keras.layers.Conv2D(filters = f3, kernel_size = 1, strides = (1,1), padding = 'valid')(X)\n",
    "    X = tf.keras.layers.BatchNormalization(axis = 3)(X, training = training) # Default axis\n",
    "    \n",
    "    # Converting the Input Volume to the match the last output for addition. \n",
    "    X_shortcut =tf.keras.layers.Conv2D(filters = f3, kernel_size = 1, strides = (s,s), padding = 'valid')(X_shortcut)\n",
    "    X_shortcut = tf.keras.layers.BatchNormalization(axis = 3)(X_shortcut, training = training)\n",
    "    X = tf.keras.layers.Add()([X_shortcut,X])\n",
    "    X = tf.keras.layers.Activation('relu')(X)\n",
    "    \n",
    "    # Adding the last two tensors\n",
    "    X = tf.keras.layers.Add()([X, X_shortcut])\n",
    "    X = tf.keras.layers.Activation('relu')(X)\n",
    "    \n",
    "    # Returning the output tensor\n",
    "    return X\n",
    "\n",
    "# Defining a modified Resnet-50 Model using the Identity and Convolution Blocks. \n",
    "def ResNet50(input_shape = (28, 28, 1), classes = 26):\n",
    "    \n",
    "    # Defining the input as a tensor with shape input_shape\n",
    "    X_input = tf.keras.Input(input_shape)\n",
    "    \n",
    "    # Zero-Padding\n",
    "    X = tf.keras.layers.ZeroPadding2D((3, 3))(X_input)\n",
    "    \n",
    "    # Stage 1\n",
    "    X = tf.keras.layers.Conv2D(64, (5, 5), strides = (1, 1))(X)\n",
    "    X = tf.keras.layers.BatchNormalization(axis = 3)(X)\n",
    "    X = tf.keras.layers.Activation('relu')(X)\n",
    "    X = tf.keras.layers.MaxPooling2D((3, 3), strides=(2, 2))(X)\n",
    "\n",
    "    # Stage 2\n",
    "    X = convolutional_block(X, f = 3, filters = [64, 64, 256], s = 1)\n",
    "    X = identity_block(X, 3, [64, 64, 256])\n",
    "    X = identity_block(X, 3, [64, 64, 256])\n",
    "    \n",
    "    # Add an Average Pool Layer\n",
    "    X = tf.keras.layers.AveragePooling2D((2,2))(X)\n",
    "\n",
    "    # Output Layer\n",
    "    X = tf.keras.layers.Flatten()(X)\n",
    "    X = tf.keras.layers.Dense(classes, activation='softmax')(X)\n",
    "    \n",
    "    # Create Model\n",
    "    model = tf.keras.Model(inputs = X_input, outputs = X)\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a4136360-0585-48af-8987-43ab131ae09b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-21T08:48:05.637825Z",
     "iopub.status.busy": "2025-09-21T08:48:05.637593Z",
     "iopub.status.idle": "2025-09-21T09:02:30.403160Z",
     "shell.execute_reply": "2025-09-21T09:02:30.402654Z",
     "shell.execute_reply.started": "2025-09-21T08:48:05.637810Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m858/858\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m91s\u001b[0m 101ms/step - accuracy: 0.8958 - loss: 0.8786 - val_accuracy: 0.7893 - val_loss: 1.7285\n",
      "Epoch 2/10\n",
      "\u001b[1m858/858\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m86s\u001b[0m 100ms/step - accuracy: 0.9866 - loss: 0.0609 - val_accuracy: 0.9044 - val_loss: 0.7627\n",
      "Epoch 3/10\n",
      "\u001b[1m858/858\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m86s\u001b[0m 100ms/step - accuracy: 0.9863 - loss: 0.0908 - val_accuracy: 0.3193 - val_loss: 32.1170\n",
      "Epoch 4/10\n",
      "\u001b[1m858/858\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m86s\u001b[0m 101ms/step - accuracy: 0.9952 - loss: 0.0315 - val_accuracy: 0.5337 - val_loss: 6.9433\n",
      "Epoch 5/10\n",
      "\u001b[1m858/858\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m86s\u001b[0m 100ms/step - accuracy: 0.9911 - loss: 0.0600 - val_accuracy: 0.9048 - val_loss: 1.0624\n",
      "Epoch 6/10\n",
      "\u001b[1m858/858\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m86s\u001b[0m 100ms/step - accuracy: 0.9983 - loss: 0.0089 - val_accuracy: 0.9615 - val_loss: 0.5860\n",
      "Epoch 7/10\n",
      "\u001b[1m858/858\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m86s\u001b[0m 100ms/step - accuracy: 0.9942 - loss: 0.0430 - val_accuracy: 0.5958 - val_loss: 5.1016\n",
      "Epoch 8/10\n",
      "\u001b[1m858/858\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m86s\u001b[0m 100ms/step - accuracy: 0.9979 - loss: 0.0119 - val_accuracy: 0.9550 - val_loss: 0.5885\n",
      "Epoch 9/10\n",
      "\u001b[1m858/858\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m85s\u001b[0m 99ms/step - accuracy: 1.0000 - loss: 6.9554e-06 - val_accuracy: 0.9566 - val_loss: 0.5908\n",
      "Epoch 10/10\n",
      "\u001b[1m858/858\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m86s\u001b[0m 100ms/step - accuracy: 1.0000 - loss: 2.5175e-06 - val_accuracy: 0.9572 - val_loss: 0.5811\n"
     ]
    }
   ],
   "source": [
    "# 初始化模型\n",
    "model = ResNet50()\n",
    "\n",
    "# 编译\n",
    "model.compile(optimizer=\"adam\",metrics=[\"accuracy\"],loss = \"sparse_categorical_crossentropy\")\n",
    "\n",
    "# 训练\n",
    "history = model.fit(train_x, train_y, validation_data = (test_x, test_y), epochs =10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b5dafaf8-4ea8-4964-9171-9b1371b88e48",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-09-21T09:43:01.030712Z",
     "iopub.status.busy": "2025-09-21T09:43:01.030481Z",
     "iopub.status.idle": "2025-09-21T09:43:01.198126Z",
     "shell.execute_reply": "2025-09-21T09:43:01.197657Z",
     "shell.execute_reply.started": "2025-09-21T09:43:01.030698Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 获取准确率与损失函数情况\n",
    "acc = history.history['accuracy']\n",
    "val_acc = history.history['val_accuracy']\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "# matplotlib绘制训练过程中指标的变化状况\n",
    "epochs = range(len(acc))\n",
    "\n",
    "plt.plot(epochs, acc, 'r', label='Training accuracy')\n",
    "plt.plot(epochs, val_acc, 'b', label='Validation accuracy')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.legend()\n",
    "plt.figure()\n",
    "\n",
    "plt.plot(epochs, loss, 'r', label='Training Loss')\n",
    "plt.plot(epochs, val_loss, 'b', label='Validation Loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b4b88470-b45f-4b0f-8f31-69a77b53510e",
   "metadata": {
    "ExecutionIndicator": {
     "show": true
    },
    "execution": {
     "iopub.execute_input": "2025-09-21T09:47:38.906677Z",
     "iopub.status.busy": "2025-09-21T09:47:38.906442Z",
     "iopub.status.idle": "2025-09-21T09:47:39.117948Z",
     "shell.execute_reply": "2025-09-21T09:47:39.117423Z",
     "shell.execute_reply.started": "2025-09-21T09:47:38.906660Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 保存\n",
    "model.save('sign_ResNet_50_tf.keras')          # 新格式\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb1113e7-425c-4e8d-b7d0-876f7714eded",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8edbe56a-b9ee-4a2b-95d3-f326d1743309",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0c1b09b-8e10-44b5-ae24-daf3dfd8aa12",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fec6281f-79aa-40f2-9619-72e514a5cf88",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "581a9f04-de48-47bf-a030-700692620f29",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
